diff --git a/samples/dnn/googlenet_python.py b/samples/dnn/googlenet_python.py
index 53ccdc5518..1db2cbf612 100644
--- a/samples/dnn/googlenet_python.py
+++ b/samples/dnn/googlenet_python.py
@@ -4,31 +4,21 @@ import cv2
 from cv2 import dnn
 import timeit
 
-def prepare_image(img):
-    img = cv2.resize(img, (224, 224))
-    #convert interleaved image (RGBRGB) to planar(RRGGBB)
-    blob = np.moveaxis(img, 2, 0)
-    blob = np.reshape(blob.astype(np.float32), (-1, 3, 224, 224))
-    return blob
-
 def timeit_forward(net):
-    print("OpenCL:", cv2.ocl.useOpenCL())
     print("Runtime:", timeit.timeit(lambda: net.forward(), number=10))
 
 def get_class_list():
     with open('synset_words.txt', 'rt') as f:
-        return [ x[x.find(" ") + 1 :] for x in f ]
+        return [x[x.find(" ") + 1:] for x in f]
 
-blob = prepare_image(cv2.imread('space_shuttle.jpg'))
+blob = dnn.blobFromImage(cv2.imread('space_shuttle.jpg'), 1, (224, 224), (104, 117, 123))
 print("Input:", blob.shape, blob.dtype)
 
-cv2.ocl.setUseOpenCL(True)  #Disable OCL if you want
 net = dnn.readNetFromCaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel')
-net.setBlob(".data", blob)
-net.forward()
+net.setInput(blob)
+prob = net.forward()
 #timeit_forward(net)        #Uncomment to check performance
 
-prob = net.getBlob("prob")
 print("Output:", prob.shape, prob.dtype)
 classes = get_class_list()
 print("Best match", classes[prob.argmax()])
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